|
Research – Publications – Teaching – Reviewing Activities
- Image Compression
- Video Compression
- Statistics of Natural Images
Journal Papers
-
P. Peter, K. Schrader, T. Alt, J. Weickert:
Deep spatial and tonal optimisation for homogeneous diffusion inpainting.
Pattern Analysis and Applications, Vol. 26, No. 4, 1585-1600, November 2023.
Invited Paper.
-
T. Alt, K. Schrader, M. Augustin, P. Peter, J. Weickert:
Connections between numerical algorithms for PDEs and neural networks.
Journal of Mathematical Imaging and Vision, June 2022.
Invited Paper.
Also available as arXiv:2107.14742 [math.NA], revised March 2022. -
T. Alt, K. Schrader, J. Weickert, P. Peter, M. Augustin:
Designing rotationally invariant neural networks from PDEs and variational methods.
Research in the Mathematical Sciences, Vol. 9, No. 3, Article 52, Sept. 2022.
Also available as arXiv:2108.13993 [cs.LG], revised March 2022. -
R. M. K. Mohideen, P. Peter, J. Weickert:
A systematic evaluation of coding strategies for sparse binary images.
Signal Processing: Image Communication, Vol. 99, Article 116424, November 2021.
Also available as arXiv:2010.13634 [eess.IV], revised July 2021. -
M. Breuß, J. Buhl, A. M. Yarahmadi, M. Bambach, P. Peter:
A simple approach to stiffness enhancement of a printable shape by Hamilton-Jacobi skeletonization.
Procedia Manufacturing, Vol. 47, 1190-1196, 2020. -
L. Hoeltgen, P. Peter, M. Breuß:
Clustering-Based Quantisation for PDE-Based Image Compression.
Signal, Image and Video Processing, Vol. 12, No. 3, 411-419, Vol. 12, No. 3, 411-419 March 2018.
Revised version of arXiv:1706.06347 [cs.CV], June 2017 -
N. Amrani, J. Serra-Sagrista, P. Peter, J. Weickert:
Diffusion-based inpainting for coding remote-sensing data.
IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 8, 1203-1207, August 2017.
Also available as Technical Report, Universitat Autonoma de Barcelona, Spain, March 2017, http://ddd.uab.cat/record/174184. -
P. Peter, L. Kaufhold, J. Weickert:
Turning diffusion-based image colorization into efficient color compression.
IEEE Transactions on Image Processing, Vol. 26, No. 2, 860-869, February 2017.
Revised version of Technical Report No. 370, Department of Mathematics, Saarland University, Saarbrücken, Germany, December 2015. -
P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
Evaluating the true potential of diffusion-based inpainting in a compression context.
Signal Processing: Image Communication, Vol. 46, 40-53, August 2016.
Revised version of Technical Report No. 373, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2016. -
P. Peter, C. Schmaltz, N. Mach, M. Mainberger, J. Weickert:
Beyond Pure Quality: Progressive Modes, Region of Interest Coding, and Real Time Video Decoding for PDE-based Image Compression.
Journal of Visual Communication and Image Representation, Vol. 31, 253-265, August 2015.
Revised version of Technical Report No. 354, Department of Mathematics, Saarland University, Saarbrücken, Germany, January 2015. -
C. Schmaltz, P. Peter, M. Mainberger, F. Ebel, J. Weickert, A. Bruhn:
Understanding, optimising, and extending data compression with anisotropic diffusion.
International Journal of Computer Vision, Vol. 108, No. 3, 222-240, July 2014.
Revised version of Technical Report No. 329, Department of Mathematics, Saarland University, Saarbrücken, Germany, March 2013. - P. Peter and M. Breuß
Refined Homotopic Thinning Algorithms and Quality Measures for Skeletonisation Methods.
M. Breuß, A. Bruckstein, P. Maragos (Eds.): Innovations for Shape Analysis: Models and Algorithms. Mathematics and Visualization, 77-92, Springer, Berlin, 2013.
Revised version of Technical Report No. 312, Department of Mathematics, Saarland University, Saarbrücken, Germany, July 2012. -
P. Bungert, P. Peter, J. Weickert:
Image blending with osmosis.
To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
-
P. Peter:
Generalised scale-space properties for probabilistic diffusion.
To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
Also available as arXiv:2303.07900 [eess.IV], March 2023. -
K. Schrader, P. Peter, N. Kämper, J. Weickert:
Efficient neural generation of 4K masks for homogeneous diffusion inpainting.
To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
-
P. Peter:
A Wasserstein GAN for joint learning of inpainting and its spatial optimisation.
To appear in Proc. 10th Pacific-Rim Symposium on Image and Video Technology (PSIVT 2022, Online Event, Nov. 2022), Lecture Notes in Computer Science, Springer, Cham, 2022.
Also available as arXiv:2202.05623 [eess.IV], February 2022. -
T. Alt, P. Peter, J. Weickert:
Learning sparse masks for diffusion-based image inpainting.
In A. J. Pinho, P. Georgieva, L. F. Teixeira, J. A. Sánchez (Eds.): Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, Vol. 13256, Springer, Cham, 528-539, 2022.
Also available as arXiv:2110.02636 [eess.IV], revised March 2022. -
S. Andris, J. Weickert, T. Alt, P. Peter:
JPEG meets PDE-based image compression.
In Proc. 35th Picture Coding Symposium (PCS 2021, Bristol, UK, June 2021), IEEE Press, 2021.
Also available as arXiv:2011.11289 [eess.IV], revised May 2021. -
P. Peter:
Quantisation scale-spaces.
In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 15-26, 2021.
Also available as arXiv:2103.10491 [eess.IV], March 2021. -
T. Alt, P. Peter, J. Weickert, K. Schrader:
Translating numerical concepts for PDEs into neural architectures.
In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 294-306, 2021.
Also available as arXiv:2103.15419 [math.NA], March 2021. -
S. Andris, P. Peter, R. M. K. Mohideen, J. Weickert, S.
Hoffmann:
Inpainting-based video compression in FullHD.
In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 12679, Springer, Cham, 425-436, 2021.
Also available as arXiv:2008.10273 [eess.IV], revised May 2021. -
F. Jost, P. Peter, J. Weickert:
Compressing piecewise smooth images with the Mumford-Shah cartoon model.
In Proc. 28th European Signal Processing Conference (EUSIPCO 2020, Amsterdam, Netherlands, January 2021), 511-515, 2021.
Also available as arXiv:2003.05206 [eess.IV], March 2020. -
F. Jost, P. Peter, J. Weickert:
Compressing flow fields with edge-aware homogeneous diffusion inpainting.
Proc. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020, Barcelona, Spain, May 2020), 2198-2202, 2020.
Also available as arXiv:1906.12263 [eess.IV], October 2019. -
P. Peter:
Fast inpainting-based compression: Combining Shepard interpolation with joint inpainting and prediction.
Proc. 2019 IEEE International Conference on Image Processing (ICIP 2019, Taipei, Taiwan, Sept. 2019), 3557-3561, 2019. -
M. Cárdenas, P. Peter, J. Weickert:
Sparsification scale-spaces.
In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods. Lecture Notes in Computer Science, Vol. 11603, 303-314, Springer, Cham, 2019. -
P. Peter, J. Contelly, J. Weickert:
Compressing audio signals with inpainting-based sparsification.
In J. Lellmann, M. Burger, J. Modersitzki (Eds.): Scale Space and Variational Methods. Lecture Notes in Computer Science, Vol. 11603, 92-103, Springer, Cham, 2019. -
L. Karos, P. Bheed, P. Peter, J. Weickert:
Optimising data for exemplar-based inpainting.
In J. Blanc-Talon, D. Helbert, W. Philips, D. Popescu, P. Scheunders (Eds.): Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol. 11182, 547-558, Springer, Cham, 2018. -
R. D. Adam, P. Peter, J. Weickert:
Denoising by inpainting.
In F. Lauze, Y. Dong, A. B. Dahl (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 10302, 121-132, Springer, Cham, 2017.
-
S. Andris, P. Peter, J. Weickert:
A proof-of-concept framework for PDE-based video compression.
Proc. 32nd Picture Coding Symposium (PCS 2016, Nuremberg, Germany, December. 2016), 1-5, 2016.
PCS 2016 Best Poster Award. -
M. Schneider, P. Peter, S. Hoffmann, J. Weickert, Enric Meinhardt-Llopis:
Gradients versus grey values for sparse image reconstruction and inpainting-based compression.
In J. Blanc-Talon, C. Distante, W. Philips, D. Popescu, P. Scheunders (Eds.): Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, Vol. 10016, 1-13, Springer, Cham, 2016.
-
P. Peter, S. Hoffmann, F. Nedwed, L. Hoeltgen, J. Weickert:
From optimised inpainting with linear PDEs towards competitive image compression codecs.
In T. Bräunl, B. McCane, M. Rivera, X. Yu (Eds.): Image and Video Technology. Lecture Notes in Computer Science, Vol. 9431, 63-74, Springer, Cham, 2016.
-
P. Peter, J. Weickert:
Compressing images with diffusion- and exemplar-based inpainting.
In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Vol. 9087, 154-165, Springer, Berlin, 2015.
-
P. Peter, J. Weickert, A. Munk, T. Krivobokova, H. Li:
Justifying tensor-driven diffusion from structure-adaptive statistics of natural images.
In X.-C. Tai, E. Bae, T. F. Chan, M. Lysaker (Eds.): Energy Minimization Methods in Computer Vision and Pattern Recognition. Lecture Notes in Computer Science, Springer, Vol. 8932, 263-277, Berlin, 2015.
- P. Peter, J. Weickert:
Colour image compression with anisotropic diffusion.
Proc. 21st IEEE International Conference on Image Processing
(ICIP 2014, Paris, France, October 2014), 4822-4826, 2014.
- P. Peter
Three-dimensional data compression with anisotropic diffusion.
J. Weickert, M. Hein, B. Schiele (Eds.): Pattern Recognition. Lecture Notes in Computer Science, Volume 8142, 231-236, Springer, Berlin, 2013.
-
D. Gaa, V. Chizhov, P. Peter, J. Weickert, R. D. Adam:
Gaining Insights into Denoising by Inpainting.
arXiv:2309.13486 [eess.IV], September 2023. -
P. Peter:
Generalised Probabilistic Diffusion Scale-Spaces.
arXiv:2309.08511 [eess.IV], September 2023. -
T. Alt, J. Weickert, P. Peter:
Translating Diffusion, Wavelets, and Regularisation into Residual Networks.
arXiv:2002.02753 [cs.LG], February 2020. -
T. Dahmen, P. Trampert, P. Peter, P. Bheed, J. Weickert, P. Slusallek:
Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting.
arXiv:1712.06326 [cs.CV], January 2020. -
P. Peter
Understanding and Advancing PDE-based Image Compression
A Dissertation Submitted Towards the Degree Doctor of Engineering (Dr.-Ing.) of the Faculties of Natural Sciences and Technology of Saarland University, Saarbrücken, Germany, January 2016.
-
P. Peter
Kompression dreidimensionaler Daten mit anisotropen Diffusionsprozessen
Thesis for High School Teachers, Dept. of Computer Science,
Saarland University, Saarbrücken, Germany, January 2011.
-
P. Peter
Hamilton-Jacobi Skeletonisation in Image Processing
Bachelor's Thesis in Computer Science, Dept. of Computer Science,
Saarland University, Saarbrücken, Germany, February 2010.
Book Chapters
Conference Papers
Preprints
Theses
If you have enjoyed the classes of our group and consider writing your thesis with us, here is some information for prospective students.
Courses
- Summer term 2023:
Lecture Image Processing and Computer Vision
Lecture Image Compression
- Winter term 2022:
Lecture Image Acquisition Methods
Lecture Advanced Image Analysis - Summer term 2022:
Lecture Image Compression
Seminar Deep Learning for Visual Computing
- Winter term 2021:
Lecture Image Acquisition Methods
Lecture Advanced Image Analysis - Summer term 2021:
Lecture Image Compression
- Winter term 2020:
Lecture Image Acquisition Methods
Lecture Advanced Image Analysis - Summer term 2020:
Lecture Image Compression
- Winter term 2019:
Lecture Image Acquisition Methods
Seminar Deep Learning: From Mathematical Foundations to Image Compression - Summer term 2019:
Lecture Image Compression
Received a Computer Science Teaching Award. - Winter term 2018:
Lecture Differential Equations in Image Processing and Computer Vision
Lecture Image Acquisition Methods
Received a Computer Science Teaching Award. - Summer term 2018:
Lecture Image Compression - Winter term 2017:
Lecture Image Acquisition Methods
Lecture Advanced Image Analysis - Summer term 2017:
Lecture Image Compression
Received a Computer Science Teaching Award.
Lecture Correspondence Problems in Computer Vision - Winter term 2016:
Lecture Differential Equations in Image Processing and Computer Vision - Summer term 2016:
Lecture Image Acquisition Methods - Winter term 2015:
Lecture Image Compression - Summer term 2015:
Lecture Image Acquisition Methods - Winter term 2014:
Lecture Image Compression - Summer term 2014:
Lecture Image Acquisition Methods
Received a Computer Science Teaching Award. - Winter term 2013:
Tutorial organisation for Image Processing and Computer Vision - Summer term 2013:
Lecture Image Acquisition Methods - Winter term 2012:
Tutorial organisation for Image Processing and Computer Vision - Summer term 2012:
Tutorial organisation for Differential Equations in Image Processing and Computer Vision
Supervised Students
In progress
- Moritz Altmeyer: M.Sc. Thesis in Computer Science.
- Paul Bungert: M.Sc. Thesis in Computer Science.
- Huasheng Chen: B.Sc. Thesis in Computer Science.
- Matthias Hock: M.Sc. Thesis in Computer Science.
- Verena Kremer: M.Sc. Thesis in Visual Computing.
- Lukas Schwitzgbel: M.Sc. Thesis in Computer Science.
- Jessica Werner: M.Sc. Thesis in Visual Computing.
Finished
- Kevin Baum: GPGPU Supportet Diffusion-Based Naive Video Compression. M.Sc. Thesis in Computer Science, 2013.
- Alexander Scheer: Entropy Coding for PDE-Based Image Compression. B.Sc. Thesis in Computer Science, 2014.
- Zhao Jin: Video Coding with Three-Dimensional Diffusion. M.Sc. Thesis in Computers and Communications, 2014
- Frank Nedwed: A Probabilistic Approach to Image Compression Using Subdivisions. B.Sc. Thesis in Computer Science, 2015.
- Hui Men: Scene-Detection for Diffusion-Based Video Compression. M.Sc. Thesis in Computer Science, 2016.
- Marie Mühlhaus: Compressing Binary Images with the Medial Axis Transform. B.Sc. Thesis in Computer Science.
- Robin Dirk Adam: Denoising by Inpainting. M.Sc. Thesis in Computer Science, 2016.
- Sreenivas Narashima Murali: Non-Local Patch based Error Measure for Texture Classification. M.Sc. Thesis in Visual Computing, 2016.
- Jan Contelly: Audio Signal Compression with Inpainting Ideas. M.Sc. Thesis in Computer Science, 2016.
- Jillian Clark: Adaptive Quantisierung für PDE-basierte Bildkompression. Thesis in Computer Science for High School Teachers, 2018.
- Merlin Köhler: Learning Optimal Data for Sparse Image Representation. M.Sc. Thesis in Computer Science, 2018.
- Vincent Kübler: Inpainting-based Compression of Noisy Images. B.Sc. Thesis in Computer Science.
- Abhishekh Goswami: Inpainting-based Compression with Simple Ingredients. M.Sc. Thesis in Visual Computing, 2018.
- Lena Karos: Optimal Data Selection for Exemplar-Based Inpainting. M.Sc. Thesis in Computer Science, 2018.
- Niklas Kämper: Encoding Strategies for Pixel Data in PDE-based Compression. B.Sc. Thesis in Computer Science, 2019.
- Rahul Mohideen: Efficient Encoding Strategies for Inpainting-Based Compression with Exact Masks. M.Sc. Thesis in Visual Computing, 2019.
- Niklas Kämper:
Neural Decoding for RJIP
M.Sc. Thesis in Computer Science, 2020. - Moritz Kunz:
Block-based Image Compression
M.Sc. Thesis in Mathematics, 2021. - Yaroslav Mykoliv:
Event Compression
M.Sc. Thesis in Computer Science, 2021.
Journals
- ACM Transactions on Graphics (TOG)
- Computers and Graphics (CG)
- Diginal Signal Processing (DSP)
- Expert Systems with Applications (ESWA)
- IEEE Transactions on Multimedia (IEEE TMM)
- IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
- IEEE Transactions on Signal Processing (IEEE TSP)
- IEEE Transactions on Very Large Scale Integration Systems (IEEE TVLSI)
- International Journal of Computer Vision (IJCV)
- Journal of Mathematical Imaging and Vision (JMIV)
- Journal of Visual Communication and Image Representation (JVCI)
- Mathematical Problems in Engineering (MPE)
- Neurocomputing
- Pattern Recognition (PR)
- SIAM Journal on Imaging Sciences (SIIMS)
- Signal Processing (SIGPRO)
- Signal Processing: Image Communication (SPIC)
- The Visual Computer (TVCJ)
Conferences
- German Conference on Pattern Recognition 2015 (GCPR 2015)
- Seventh International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019)